##‘geom_function’

The “geom_function” can be used to draw functions in ggplot2. Computes and draws a function as a continuous curve. This makes it easy to superimpose a function on top of an existing plot. The function is called with a grid of evenly spaced values along the x axis,and the results are drawn (by default) with a line.

Illustration

ggplot() + xlim(c(0,5))+geom_function(fun=cos,colour="red",lwd=1,linetype=1)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

set.seed(1492)
ggplot(data.frame(x = rnorm(100)),aes(x))+ geom_density() + geom_function(fun = dnorm, colour = "red")

base<- ggplot()+ xlim(-5,5)

base + geom_function(fun = dnorm)

base +
geom_function(aes(colour = "normal"), fun = dnorm) + geom_function(aes(colour = "t, df=1"), fun = dt, args = list(df=1))

##‘geom_dl’

Illustration

library(directlabels)

giris <- ggplot(iris,aes(Petal.Length,Sepal.Length))+
  geom_point(aes(shape=Species))
giris.labeled <- giris+
  geom_dl(aes(label=Species),method="smart.grid")+
  scale_shape_manual(values=c(setosa=1,virginica=6,versicolor=3),
                     guide="none")
print(giris.labeled)

##‘geom_encircle()’

we can automatically enclose data points in a polygon.

Illustration

library(ggalt)
## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2
PL <- iris$Petal.Length
PW <- iris$Petal.Width
ggplot(iris,aes(x=PL,y=PW)) + geom_point(col="green") + geom_encircle(s_shape=0.2, expand=0.01,fill="Red",alpha=0.4) +labs(title = "Enclosed Scatter Plot of Sepal-width vs Sepal-Length",x="Sepal Length",y="Sepal Width")

##‘geom_mark_circle()’

Geom Mark Circle lets you annotate sets of points via circles. The enclosing circles are calculated at draw time and the most optimal enclosure at the given aspect ratio is thus guaranteed

Illustration

library(ggforce)
ggplot(iris, aes(Petal.Length, Petal.Width)) +
  geom_mark_circle(aes(fill = Species, label = Species),
                   con.cap = 0) +
  geom_point()
## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

##‘geom_delaunay_tile’

geom_delaunay_tile allows us to display polygons and as line segments

Illustration

library(ggforce)
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_delaunay_tile(alpha = 0.5, colour = 'green')
## Warning: `stat_delaunay_tile()` is dropping duplicated points

##‘geom_label_repel’

Illustration

library(ggrepel)
set.seed(45)
ggplot(mtcars) +
  geom_point(aes(wt, mpg), size = 4, color = 'blue') +
  geom_label_repel(aes(wt, mpg, fill = factor(cyl), label = rownames(mtcars)),
                   fontface = 'bold', color = 'grey',
                   box.padding = unit(0.40, "lines"),
                   point.padding = unit(0.6, "lines"),
                   segment.color = 'grey50'
  ) +
  theme_classic(base_size = 20)
## Warning: ggrepel: 19 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

##‘geom_map’

geom_map is pure annotation, So does not affect position scales.It can be used to draw the map for different regions in the world.

Illustration

library(maps)
## 
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
## 
##     map
world<-map_data("world")
ggplot()+geom_map(data=world,map=world,aes(x=long,y2=lat,map_id=region),col="white",fill="grey")
## Warning in geom_map(data = world, map = world, aes(x = long, y2 = lat, map_id =
## region), : Ignoring unknown aesthetics: x and y2

##‘geom_sina’

The sina plot is a data visualization chart suitable for plotting any single variable in a multiclass dataset. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.

Illustration

# pacman::p_load(
#   tidyverse,      
#   rio,            
#   here,          
#   stringr            
# )
# 
# linelist <- rio::import("linelist_cleaned.rds")
# library(tidyr)
# 
# ggplot(
#   data = linelist %>% drop_na(outcome),
#   aes(y = age,          
#       x = outcome)) +    
#   geom_violin(
#     aes(fill = outcome),
#     color = "white",    
#     alpha = 0.2)+        
#   geom_sina(
#     size=1,                
#     aes(color = outcome))+
#   scale_fill_manual(       # Define fill for violin background by death/recover
#     values = c("Death" = "#bf5300",
#                "Recover" = "#11118c")) +
#   scale_color_manual(      # Define colours for points by death/recover
#     values = c("Death" = "#bf5300",
#                "Recover" = "#11118c")) +
#   theme_minimal() +                                
#   theme(legend.position = "none") +                
#   labs(title = "B) violin and sina plot by gender, with extra formatting")

##‘geom_lm’

geom_lm() use for draw a regression line. In this case it draws the most suitable fitted regression line.

Illustration

library(ggformula)
## Loading required package: ggstance
## 
## Attaching package: 'ggstance'
## The following objects are masked from 'package:ggplot2':
## 
##     geom_errorbarh, GeomErrorbarh
## Loading required package: scales
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
## Loading required package: ggridges
## 
## New to ggformula?  Try the tutorials: 
##  learnr::run_tutorial("introduction", package = "ggformula")
##  learnr::run_tutorial("refining", package = "ggformula")
## 
## Attaching package: 'ggformula'
## The following objects are masked from 'package:ggalt':
## 
##     stat_ash, StatAsh
ggplot(iris,aes(x=iris$Petal.Width,y=iris$Petal.Length)) + geom_point(col="green") +labs(title = "Scatter Plot of Sepal-Length vs Sepal-width",x="Sepal width",y="Sepal length") + geom_lm()
## Warning: Use of `iris$Petal.Width` is discouraged.
## ℹ Use `Petal.Width` instead.
## Warning: Use of `iris$Petal.Length` is discouraged.
## ℹ Use `Petal.Length` instead.
## Warning: Use of `iris$Petal.Width` is discouraged.
## ℹ Use `Petal.Width` instead.
## Warning: Use of `iris$Petal.Length` is discouraged.
## ℹ Use `Petal.Length` instead.
## Warning: Using the `size` aesthietic with geom_line was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.

##‘geom_circle’

geom_circle possible to draw circles based on a center point and a radius. ### Illustration

library(ggforce)
sample = data.frame(shot_x = c(10, 20), shot_y = c(30, 40))
ggplot(sample, aes(shot_x, shot_y)) +
  coord_fixed(ratio = 1) +
  geom_circle(aes(x0 = 47, y0 = 25, r = 6), inherit.aes = FALSE)

##‘geom_errorbarh’ geom_errorbarh is used to illustrate horizontal error bars on a plot (representing variability of the plotted data in the horizontal direction).

Illustration

sample_data <- head(iris)
spl_len <- sample_data[1:6,1]
se <- sd(spl_len)/sqrt(length(spl_len))

# plot codes

p <- ggplot(sample_data, aes(Sepal.Length, Sepal.Width))
p +
  geom_point(colour = "darkorange2", size = 3) +
  geom_errorbarh(aes(xmax = Sepal.Length + se, xmin = Sepal.Length - se),colour = "blue4")
## Warning: Using the `size` aesthietic with geom_path was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.

##’geom__lm_intercept’ geom_lm_intercept is can be used to identified the intercept of given two variables.

Illustration

library(ggxmean)
## 
## Attaching package: 'ggxmean'
## The following object is masked from 'package:ggformula':
## 
##     geom_lm
ggplot(iris,aes(Petal.Length,Petal.Width))+geom_point()+geom_lm_intercept()

##‘geom_voronoi_tile’ geom_voronoi_tile allows us to display voronoi tesselation and delaunay triangulation, both as polygons and as line segments.

Illustration

library(ggforce)

ggplot(iris, aes(Sepal.Length, Sepal.Width, group = -2L)) +
  geom_voronoi_tile(aes(fill = Species), colour = 'orange',
                    expand = unit(-.8, 'mm'), radius = unit(3, 'mm'))
## Warning: `stat_voronoi_tile()` is dropping duplicated points

##‘geom_x_line’ geom_x_line is used to annotate the plot with vertical lines.

Illustration

library(ggxmean)
ggplot(iris,aes(Petal.Length,Petal.Width))+geom_point(col="red")+geom_x_line()
## Warning: Using the `size` aesthietic with geom_segment was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.

##‘geom_dumbbell’ The dumbbell plot shows the change between two points in a data set. it helps us to understand the span of data categorically. ### Illustration

library(ggalt)

ylabel <- c("first","second","third")
x1 <- c(1,2,3)
x2 <- c(4,3,5)
datamain <- data.frame(ylabel,x1,x2)

ggplot() + geom_dumbbell(data = datamain,
                         aes(y = ylabel,
                             x = x1,
                             xend = x2),
                         size = 1.5)

##‘geom_path’ geom_path() connects the observations in the order in which they appear in the data.

Illustration

.

ggplot(data=mpg,aes(x=displ,y=hwy,col=drv))+geom_path()

##‘geom_alluvium’ The geom_alluvium is a ggplot2 extension for producing alluvial plots in tidyverse framework.The design and functionality were originally inspired by the alluvial package. It can be used to visualize frequency distributions over time or frequency tables involving several categorical variables.

Illustration

library(ggalluvial)

ggplot(as.data.frame(Titanic),
       aes(y=Freq,
           axis1=Class, axis2= Sex, axis3= Age, fill=Survived))+
geom_alluvium()+ scale_x_discrete(limits= c("Class", "Sex", "Age"))

##‘geom_qq_line’ geom_qq() produce quantile-quantile plots. geom_qq_line() compute the slope and intercept of the line connecting the points at specified quartiles of the theoretical and sample distributions.geom_qq_line() and stat_qq_line() do the same thing. ### Illustration

norms <- rnorm(150)
fx <- data.frame(norms)
fx$parts <- factor(sample(1:3, 150, replace = T))
ggplot(fx,aes(sample = norms, col = parts)) + 
  geom_qq() +
  geom_qq_line()

##‘geom_xy_means’ geom_xy_means can be used to place poit at mean of x and mean of y. ### Illustration

library(ggxmean)
ggplot(iris, aes(Sepal.Length,Sepal.Width )) + geom_point(col="blue" , alpha=0.5) + geom_xy_means(col="red" ,shape= 8 , alpha=10)

##‘geom_heat_tri’ The above heattriangle geom is used to create the two triangles split by a diagonal line of a rectangle that use luminance to show the values from two sources on the same plot.

Illustration

library(ggDoubleHeat)

data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   lower_values = rep(c(1,5,7),3),
                   upper_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_tri(lower = lower_values, upper = upper_values)

##‘geom_col’

geom_col can be used to mapped values for positional parameters x and y directly to variables from the selected data set.

Illustration

data(diamonds)

ggplot(data=diamonds,aes(x=color,y=price,fill=color))+geom_col()+coord_flip()

##‘geom_sankey’ A sankey diagram is a visualization used to depict a flow from one set of values to another.

Illustration

library(ggsankey)

df <- mtcars %>%
  make_long(mpg, disp, cyl, vs, am)

ggplot(df, aes(x = x,
               next_x = next_x,
               node = node,
               next_node = next_node,
               fill = factor(node))) +
  geom_sankey()

##‘geom_bar_interactive’ This geom is based on geom_bar(). In this way they can be set to a scalar value.

Illustration

library(ggiraph)
ggplot(diamonds) +
  geom_bar_interactive(aes(x = cut, fill = clarity), position = "dodge")

##‘geom_line_interactive’ The geometry is based on geom_line(). It is used to create interactive lines on the graph.

Illustration

 library(ggiraph)

df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("D0.5", "D1", "D2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))

# Change line types by groups (supp)
ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()

ggplot(df2, aes(x=dose, y=len, group=supp)) +
  geom_line(aes(linetype=supp))+
  geom_point()+ geom_line_interactive(size = 3,
                        alpha = 0.2)

##‘geom_label’ geom_label() draws a rectangle behind the text, making it easier to read.

Illustration

ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars))) +
  geom_label(aes(fill = factor(cyl)), colour = "white", fontface = "bold")

##‘geom_quantile’

geom_quantile is capable of showing more than just the conditional median. Given the small sample size for data set it’s wise not to draw too many conclusions, this is meant to illustrate the purpose of quantile regression. We can also adjust the line’s appearance.

Illustration

# library(readxl)
# mtcars <- read_excel("mtcars.xlsx")
# View(mtcars)
# 
# m <-
#   ggplot(mpg, aes(displ, 1/hwy))
# m + geom_quantile()

##‘geom_y_line’ geom_y_line is used to annotate the plot with horizontal lines.

Illustration

library(ggxmean)

ggplot(iris,aes(Sepal.Length,Sepal.Width))+geom_point()+geom_y_line(col="forestgreen")

##’geom__heat_circle’ The heatcircle geom is used to create the two concentric circles that use luminance to show the values from two sources on the same plot

Illustration

library(ggDoubleHeat)
data <- data.frame(x = rep(c("a", "b", "c"), 3),
                   y = rep(c("d", "e", "f"), 3),
                   outside_values = rep(c(1,5,7),3),
                   inside_values = rep(c(2,3,4),3))

ggplot(data, aes(x,y)) +
geom_heat_circle(outside = outside_values,
                 inside = inside_values)

##‘geom_quasirandom’ The quasirandom geom is a convenient means to offset points within categories to reduce overplotting. Overplotting is when the data in a data visualization overlap, making it difficult to see individual data points in a data visualization. Overplotting typically occurs when there are either a large number of data points and/or a small number of unique values in the dataset. So to reduce this overplotting within categories, we can use geom_quasirandom() function. ### Illustration

library(ggbeeswarm)
#generate data
variables<-rep(c('runif','rnorm'),each=400)
values<-c(runif(400, min=-3, max=3), rnorm(400))
distro <- data.frame(variables,values)

ggplot(data = distro)+
  geom_quasirandom(aes(x=variables,y=values,color=variables))

library(mosaicData)
data(Births)

ggplot(data = Births,mapping = aes(x = wday, y = births)) +
       geom_quasirandom(color="darkblue",size=0.001)  # defined size to make data points super tiny

##‘geom_ridgeline’ The geom_ridgeline can be used to draw lines with a filled area underneath.

Illustration

library(ggridges)
data<-data.frame(x=1:5, y=rep(1,5), height = c(0,1,3,4,2))
ggplot(data,aes(x,y, height = height)) + geom_ridgeline()

##‘geom_contour’ ggplot2 can not draw true 3D surfaces, but you can use geom_contour(), geom_contour_filled(), and geom_tile() to visualise 3D surfaces in 2D. ### Illustration

v <- ggplot(faithfuld, aes(waiting, eruptions, z = density)) 
v + geom_contour()

##’geom__sf_lable()’ geom_sf_lable() can be used to add labels on the each region

Illustration

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)

nc3 <- nc[1:3, ]      

p <- ggplot(nc3) + geom_sf(aes(fill = AREA))

p + geom_sf_label(aes(label = NAME))
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

##‘geom_arc & geom_arc_bar’

geom_arc function makes it possible to draw circle segments based on a center point, a radius and a start and end angle (in radians).These functions are intended for cartesian coordinate systems and makes it possible to create circular plot types.An arc is a segment of a line describing a circle. It is the fundamental visual element in donut charts where the length of the segment (and conversely the angular span of the segment) describes the proportion of an entity.

geom_arc_bar function makes it possible to draw arcs and wedges as known from pie and donut charts as well as more specialized plot types such as sunburst plots.An arc bar is the thick version of an arc; that is, a circle segment drawn as a polygon in the same way as a rectangle is a thick version of a line. A wedge is a special case of an arc where the inner radius is 0.Most notable of these are the option to explode arcs and wedgets away from their center point, thus detaching it from the main pie/donut.

Illustration

library(ggforce)


 # knowing the angle span makes plotting it is easy
arcs <- data.frame(
  start = seq(0, 2 * pi, length.out = 11)[-11],
  end = seq(0, 2 * pi, length.out = 11)[-1],
  r = rep(1:2, 5)
)

# Behold the arcs
ggplot(arcs) +
  geom_arc(aes(x0 = 0, y0 = 0, r = r, start = start, end = end,
               linetype = factor(r)))

# Behold the arcs
ggplot(arcs) +
  geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = r - 1, r = r, start = start,
                   end = end, fill = r))

##‘geom_area’ One of the main uses of geom_area() is to plot area charts with suitable variables for x-axis and y-axis. It is a special case of geom_ribbon()

Illustration

data_set <- mpg
ggplot(data_set) + geom_area(aes(x=seq_along(cty), y=cty), fill=4, alpha=0.5)

##‘geom_abline’ The geom_abline adds a line with specified slope and intercept to the plot. These values can be defined as numeric values, can be defined by a function or can be mapped from an important data set.

Illustration

ggplot(iris, aes(x=Sepal.Length,y=Sepal.Width))+geom_point() +xlab("Sepal length (cm)") +
  ylab("Sepal width (cm)") +
  ggtitle("Correlation between Sepal length and width") + geom_abline(intercept = 0 , slope = 1 )

##‘geom_bump’

Illustration

library(ggbump)
parks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-22/parks.csv')
## Rows: 713 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): city, park_pct_city_data, pct_near_park_data, spend_per_resident_d...
## dbl (23): year, rank, med_park_size_data, med_park_size_points, park_pct_cit...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
parks_df <- parks %>%
  mutate(park_pct_city_data = parse_number(park_pct_city_data),
         pct_near_park_data = parse_number(pct_near_park_data),
         spend_per_resident_data = parse_number(spend_per_resident_data)) %>%
  mutate(across(where(is.character), factor)) %>%
  select(-city_dup)

texas_cities <- c("Dallas", "Austin", "Arlington, Texas", "Fort Worth",
                  "El Paso", "Houston", "San Antonio")

# create a dataset that is filtered by Texas cities
texas_parks <- parks_df %>%
  filter(city %in% texas_cities)

texas_parks %>%
  ggplot(aes(year, rank, color = city)) +
  geom_point(size = 5) +
  geom_bump() +
  theme_minimal() +
  scale_y_reverse()

##‘geom_mark_rect’

geom_mark_rect() allows you to annotate sets of points via rectangles.

Illustration

library(ggforce)

ggplot(iris,
       aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
      geom_point()+
  geom_mark_rect()

##‘geom_mark_hull’

Geom_mark_hull annotates sets of points via hulls.Also geom_mark_hull uses concaveman which adjusts concavity of the resulting hull. The hull is calculated at draw time, and can thus change as you resize the plot. In order to clearly contain all points, and for aesthetic purpose the resulting hull is expanded 5mm and rounded on the corners. This can be adjusted with the expand and radius parameters.

Illustration

library(concaveman)
library(ggforce)

ggplot(iris,
       aes(x=Sepal.Length,y=Sepal.Width,col=Species))+
  geom_point()+
  geom_mark_hull()

##‘geom_stream’ The geom_stream allows creating streamplots in ggplot2. ggstream is creating a simple implementation of streamplots/graph in ggplot2. ### Illustration

library(ggstream)
ggplot(blockbusters, aes(year,box_office,fill=genre))+geom_stream()

##’geom__qq’ Quantile-quantile plots known as qq plots. qq plots are used to visually estimate whether a sample distribution is normal,in which case the quantiles are nicely aligned in the plot.

Illustration

library(MASS)
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
ggplot(anorexia,aes(sample=Prewt,colour=factor(Treat)))+geom_qq()+labs(title="QQ plot of weight of patient before study period by treatments",x="theoreticles",y="Norm samples")+geom_qq_line()

##‘geom_lm_conf_int’ geom_lm_conf_int() can be use to see the confidence interval ### Illustration

library(ggxmean)
library(colmozzie)

ggplot(colmozzie, aes( x=Week ,y=TMAX))+geom_point() +geom_lm_conf_int()+geom_lm()

##‘geom_sf’

The geom_sf is used to visualize simple feature (sf) objects in ggplot2 . geom_sf() is an unusual geom because it will draw different geometric objects depending on what simple features are present in the data. This works reasonably well when you need to draw polygons, like our state boundaries. Syntax of geom_sf, geom_sf(mapping = aes(), data = NULL, stat = “sf”, position = “identity”, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, …)

Illustration

library(maps)
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(rnaturalearth)
world <- ne_countries(returnclass= "sf")
ggplot(data = world)+geom_sf(aes() , colour = "black" , fill = "yellow")

##‘geom_lm_pred_int_segments’ geom_lm_pred_int_segments is used to identify for OLS linear model as segments ### Illustration

library(ggxmean)

ggplot(iris,aes(Sepal.Length,Petal.Width))+geom_lm_pred_int_segments()

##‘geom_textdensity’ geom_textdensity() produces the density plots and simply labels each density curve.

Illustration

library("geomtextpath")
ggplot(data=mpg, aes(displ, label = drv, col = drv)) + geom_textdensity()

##‘geom_density_ridges’

geom_density_ridges arranges density plot in a straggered fashion. The geom_density_ridges calculates density estimates from the provided data and then plot those using the ridgeline visualization.

Illustration

library(ggridges)
library(ggstream)

ggplot(blockbusters,aes(x=box_office,y=genre,fill=genre))+geom_density_ridges(scale=2)
## Picking joint bandwidth of 0.456

##‘geom_lm_intercept_label’ geom_lm_intercept_label is can be used to label OLS linear model intercept.

Illustration

library(ggxmean)
ggplot(iris,aes(Petal.Length,Petal.Width))+geom_point()+geom_lm_intercept_label()

##‘geom_contour_filled’ we can not use ggplot2 to draw true 3D surfaces,but we can use geom_contour_filled() to visualize 3D surfaces in 2D.Data must contain X,Y,Z coordinates to specify a valid surface and each unique combination of x and y can appearat once. ### Illustration

ggplot(iris, aes(x=Sepal.Length,y=Sepal.Width,z =Petal.Length ))+
  geom_contour_filled()

##‘geom_textline’ We can use geom_textline as a drop in for geom_line if we want it directly labelled.We can specify the line’s appearence and the text’s appearance separately. ### Illustration

library(geomtextpath)
ggplot(economics, aes(date, unemploy)) +
  geom_textline(linecolour= "grey", size = 4, vjust = -1, hjust = 0.35,
                label = "1990s Decline", text_smoothing = 30)
## Warning: The text offset exceeds the curvature in one or more paths. This will result in
## displaced letters. Consider reducing the vjust or text size, or use the hjust
## parameter to move the string to a different point on the path.

##‘geom_lm_residuals’ geom_lm_residuals is the difference between the observed value and the fitted value that the model predicts for that observation.

Illustration

library(ggxmean)

ggplot(mtcars, aes(mpg, qsec))+geom_point()+geom_lm_residuals()

##‘geom_bin2d’ Displays a 1d distribution by dividing variable mapped to x axis into rectangles and counting the number of observations in each rectangle.

Illustration

library(gapminder)
ggplot(gapminder,
aes(x=lifeExp, y=gdpPercap, col=year))+
geom_bin2d( col = "forestgreen") +
theme(legend.position = "bottom")+
labs(title = "Relationship between life expectency and gdp per capita",
     x = "Life expentency at birth in years",
     y = "GDP per Capita")

##‘geom_shape’ This geom is a cousin of ggplot2::geom_polygon() with the added possibility of expanding or contracting the polygon by an absolute amount (e.g. 1cm). Furthermore, it is possible to round the corners of the polygon, again by an absolute amount. The resulting geom reacts to resizing of the plot, so the expansion/contraction and corner radius will not get distorted.

Illustration

shape <- data.frame(
  x = c(-0.5, 1, 0.75, 0.25, 0),
  y = c(0, -0.5, 1, 0.75, 0.25)
)
# Expand and round
ggplot(shape, aes(x = x, y = y)) +
  geom_shape(expand = unit(1, 'cm'), radius = unit(0.5, 'cm')) +
  geom_polygon(fill = 'blue')